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Bayesian Prediction with a Cointegrated Vector Autoregression

  • Villani, Mattias

    ()

    (Department of Statistics, Stockholm University)

A complete procedure for calculating the joint predictive distribution of future observations based on the cointegrated vector autoregression is presented. The large degree of uncertainty in the choise of the cointegration vectors is incorporated into the analysis through a prior distribution on the cointegration vectors which allows the forecaster to realistically express his beliefs. This prior leads to a form of model averaging where the predictions from the models based on the different cointegration vectors are weighted together in an optimal way. The ideas of Litterman (1980) are adapted for the prior on the short run dynamics with a resulting prior which only depends on a few hyperparameters and is therefore easily specified. A straight forward numerical evaluation of the predictive distribution based on Gibbs sampling is proposed. The prediction procedure is applied to a seven variable system with focus on forecasting the Swedish inflation.

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Paper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 97.

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Length: 23 pages
Date of creation: 01 Oct 1999
Date of revision:
Publication status: Published in International Journal of Forecasting, 2001, pages 585-605.
Handle: RePEc:hhs:rbnkwp:0097
Contact details of provider: Postal: Sveriges Riksbank, SE-103 37 Stockholm, Sweden
Phone: 08 - 787 00 00
Fax: 08-21 05 31
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